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Found 7 papers, 3 papers with code
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation
To this end, we employ domain adversarial learning as a heuristic neural network initialization method, which can help the meta-learning module converge to a better optimal.
Meta-Tsallis-Entropy Minimization: A New Self-Training Approach for Domain Adaptation on Text Classification
Theoretically, we prove the convergence of the meta-learning algorithm in MTEM and analyze the effectiveness of MTEM in achieving domain adaptation.
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions.
Graph Pooling via Coarsened Graph Infomax
Graph pooling that summaries the information in a large graph into a compact form is essential in hierarchical graph representation learning.
WGCN: Graph Convolutional Networks with Weighted Structural Features
Based on nodes' geometrical relationships in the latent space, WGCN differentiates latent, in-, and out-neighbors with an attention-based geometrical aggregation.
HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks
Experimental results show that compared with the state-of-the-art models, which imitate hexagonal processing but using rectangle-shaped filters, HexCNN reduces the training time by up to 42. 2%.
CBHE: Corner-based Building Height Estimation for Complex Street Scene Images
CBHE first obtains building corner and roofline candidates in street scene images based on building footprints from 2D maps and the camera parameters.
